Adaptive Resource Management Based on Reinforcement Learning

Abstract

This paper investigates the application of adaptive reinforcement learning (RL) for efficient hardware resource management. Traditional hardware resource management often relies on static configurations and manual tuning, which can be inefficient and unresponsive to dynamic workloads. This research proposes a novel approach utilizing RL to autonomously learn and optimize resource allocation and scheduling policies. The core idea is to model the hardware resource management problem as an RL problem, where an agent interacts with the environment (hardware resources) and learns through trial and error to maximize a defined reward function. The presented framework offers the potential for significant improvements in resource utilization, reduced latency, and enhanced overall system performance. Specifically, we explore the application of multi-agent reinforcement learning to manage CPU, memory, and network bandwidth simultaneously, addressing the complexities of modern heterogeneous systems. The evaluation of the proposed method demonstrates its effectiveness in adapting to varying workloads and achieving superior resource allocation compared to conventional methods. ---